Prenatal exposure to legacy contaminants and visual acuity in Canadian infants: a maternal-infant research on environmental chemicals study (MIREC-ID)
Bibliographic record
Abstract
Abstract Background Prenatal exposure to environmental contaminants can have deleterious effects on child development. While psychomotor, cognitive and behavioural outcomes have been investigated in relation to chronic exposure, the associations with visual functions remains unclear. The present study’s aim was to assess the associations of prenatal exposure to legacy persistent organic pollutants and heavy metals with visual acuity in Canadian infants. The potential protective effects of selenium against mercury toxicity were also examined. Methods Participants (mean corrected age = 6.6 months) were part of the Maternal-Infant Research on Environmental Chemicals (MIREC) study. Concentrations of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), lead and mercury were measured in maternal blood during pregnancy, as well as in the cord blood. The Teller acuity card test (TAC) (n = 429) and the visual evoked potentials in a sub-group (n = 63) were used to estimate behavioural and electrophysiological visual acuity, respectively. Multivariable linear regression models were used to investigate the relationship between exposure to each contaminant and visual acuity measures, while controlling for potential confounders. Breastmilk selenium, which was available for about half of the TAC and VEP samples, was also taken into account in the mercury models as exploratory analyses. Results We observed no significant associations between exposure to any contaminants and TAC. Analyses revealed a negative trend (p values
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".